Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional …

Given the large variety and complexity of tables, table structure extraction is a challenging
task in automated document analysis systems. We present a pair of novel deep learning …

In social media, demographic inference is a critical task in order to gain a better
understanding of a cohort and to facilitate interacting with one's audience. Most previous …

We present in this paper experiments on Table Recognition in hand-written register books.
We first explain how the problem of row and column detection is modelled, and then …

Abstract OCR (Optical Character Recognition) for scanned paper invoices is very
challenging due to the variability of 19 invoice layouts, different information fields, large data …

Deep learning network (DLN) is defined as the neural network characterized by complex
connected layers to handle a large volume of data, automatic extraction of features, and …

Vertex classification is a critical task in graph analysis, where both contents and linkage of
vertices are incorporated during classification. Recently, researchers proposed using deep …

We consider the Document Understanding problem of segmenting tables in rows. We
propose a method that first enumerates virtual row separator candidates and then select the …

A deep-learning based method evaluates similarities of entities in decentralized identity
graphs. One or more processors represent a first identity profile as a first identity graph and a …

(57) ABSTRACT A method provides a security action based on identity profile scores. One or
more processors represent an identity profile as a knowledge graph. The processor (s) …